Black women are not monolithic. Though we had some things in common, we had our differences as well. Some of the participants were married, and some of them had children. While money was a factor for majority of participants and myself. There were a few participants who had substantial aid packages that covered tuition and provided them with a stipend. Another noticeable difference was the type of support received from dissertation advisors. Many participants discussed the lack of support from their dissertation advisor, while a few discussed receiving the support, guidance, and encouragement that they needed from their advisors.
Another challenge I came across while doing data collection was that I found myself identifying or defining a participant’s experience to them. For example, when someone talked about a professor noting how smart they were or how well-spoken they were as a Black woman, I tended to identify it as micro-aggression. Many participants agreed and said things such as “yes, that’s what that is” or “what does that mean exactly.” I processed my concerns with my colleague and was able to reframe from identifying participants experiences. My hope in doing this was to allow the participants to identify and describe their experiences from their perspective.
Data Analysis
This section will describe how the data obtained through the interview process was managed followed by an exploration of how the data was then coded and separated into themes. Lastly, this section will also describe efforts taken to increase credibility and trustworthiness.
Data Analysis.Each interview was audiotaped and transcribed. I choose to transcribe all of the interviews because it gave me the opportunity to fill in unclear passages and to insert explanations or clarifications (Padgett, 2008). Completing my own transcriptions was also cost efficient but though time-consuming. The amount of time I spent with the data was increased by listening to each transcription multiple times to make sure that I did not miss any information. Through this process, I became more familiar with participant voices and was allowed the opportunity to hear the transcription with a new perspective after starting my code book.
I transcribed the first two interviews directly from the tape recorder and the remaining eighteen were transcribed utilizing the Transcribe software (https://transcribe.wreally.com/). This software allowed me to upload each audio interview and it provides a text editor that simplifies the playback options (pause, play, fast-forward, rewind, and timestamp) while transcribing the interview in one place instead of going between your tape recorder and a word document. Transcribe allowed me to cut down on the amount of time that it took to transcribe the remaining interviews. The audio worked off my local browser and I never had to send the interviews to a third party, thus maintaining participants’ confidentiality. The software also allowed me to export the text to a Word document, making it easier to keep up with all the transcriptions. The software has a diction option but I choose not to utilize it due to time constraints.
After the interviews were transcribed, I reviewed them for accuracy and then changed and participants’ names so that they would not be easily identified. All participants were given a pseudonym of a historical Black female social worker/advocate in order to fully disguise participants (Padgett, 2008). Each participant was then sent their corresponding transcription and was asked to review it for accuracy and clarity. They were also encouraged to make any changes using notes in
